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  <h1>Source code for openspeech.data.audio.data_loader</h1><div class="highlight"><pre>
<span></span><span class="c1"># MIT License</span>
<span class="c1">#</span>
<span class="c1"># Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho</span>
<span class="c1">#</span>
<span class="c1"># Permission is hereby granted, free of charge, to any person obtaining a copy</span>
<span class="c1"># of this software and associated documentation files (the &quot;Software&quot;), to deal</span>
<span class="c1"># in the Software without restriction, including without limitation the rights</span>
<span class="c1"># to use, copy, modify, merge, publish, distribute, sublicense, and/or sell</span>
<span class="c1"># copies of the Software, and to permit persons to whom the Software is</span>
<span class="c1"># furnished to do so, subject to the following conditions:</span>
<span class="c1">#</span>
<span class="c1"># The above copyright notice and this permission notice shall be included in all</span>
<span class="c1"># copies or substantial portions of the Software.</span>
<span class="c1">#</span>
<span class="c1"># THE SOFTWARE IS PROVIDED &quot;AS IS&quot;, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR</span>
<span class="c1"># IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,</span>
<span class="c1"># FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE</span>
<span class="c1"># AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER</span>
<span class="c1"># LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,</span>
<span class="c1"># OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE</span>
<span class="c1"># SOFTWARE.</span>

<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span><span class="p">,</span> <span class="n">Sampler</span>


<span class="k">def</span> <span class="nf">_collate_fn</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">pad_id</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Functions that pad to the maximum sequence length</span>

<span class="sd">    Args:</span>
<span class="sd">        batch (tuple): tuple contains input and target tensors</span>
<span class="sd">        pad_id (int): identification of pad token</span>

<span class="sd">    Returns:</span>
<span class="sd">        seqs (torch.FloatTensor): tensor contains input sequences.</span>
<span class="sd">        target (torch.IntTensor): tensor contains target sequences.</span>
<span class="sd">        seq_lengths (torch.IntTensor): tensor contains input sequence lengths</span>
<span class="sd">        target_lengths (torch.IntTensor): tensor contains target sequence lengths</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="nf">seq_length_</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>

    <span class="k">def</span> <span class="nf">target_length_</span><span class="p">(</span><span class="n">p</span><span class="p">):</span>
        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>

    <span class="c1"># sort by sequence length for rnn.pack_padded_sequence()</span>
    <span class="n">batch</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">sample</span><span class="p">:</span> <span class="n">sample</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

    <span class="n">seq_lengths</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>
    <span class="n">target_lengths</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">]</span>

    <span class="n">max_seq_sample</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="n">seq_length_</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
    <span class="n">max_target_sample</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="n">target_length_</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>

    <span class="n">max_seq_size</span> <span class="o">=</span> <span class="n">max_seq_sample</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">max_target_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">max_target_sample</span><span class="p">)</span>

    <span class="n">feat_size</span> <span class="o">=</span> <span class="n">max_seq_sample</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">batch_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>

    <span class="n">seqs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">max_seq_size</span><span class="p">,</span> <span class="n">feat_size</span><span class="p">)</span>

    <span class="n">targets</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">max_target_size</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
    <span class="n">targets</span><span class="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="n">pad_id</span><span class="p">)</span>

    <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">batch_size</span><span class="p">):</span>
        <span class="n">sample</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="n">x</span><span class="p">]</span>
        <span class="n">tensor</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">target</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
        <span class="n">seq_length</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>

        <span class="n">seqs</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">narrow</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">seq_length</span><span class="p">)</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span>
        <span class="n">targets</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">.</span><span class="n">narrow</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">target</span><span class="p">))</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">LongTensor</span><span class="p">(</span><span class="n">target</span><span class="p">))</span>

    <span class="n">seq_lengths</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">IntTensor</span><span class="p">(</span><span class="n">seq_lengths</span><span class="p">)</span>
    <span class="n">target_lengths</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">IntTensor</span><span class="p">(</span><span class="n">target_lengths</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">seqs</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">seq_lengths</span><span class="p">,</span> <span class="n">target_lengths</span>


<div class="viewcode-block" id="AudioDataLoader"><a class="viewcode-back" href="../../../../modules/Data Loaders.html#openspeech.data.audio.data_loader.AudioDataLoader">[docs]</a><span class="k">class</span> <span class="nc">AudioDataLoader</span><span class="p">(</span><span class="n">DataLoader</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Audio Data Loader</span>

<span class="sd">    Args:</span>
<span class="sd">        dataset (torch.utils.data.Dataset): dataset from which to load the data.</span>
<span class="sd">        num_workers (int): how many subprocesses to use for data loading.</span>
<span class="sd">        batch_sampler (torch.utils.data.sampler.Sampler): defines the strategy to draw samples from the dataset.</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
            <span class="bp">self</span><span class="p">,</span>
            <span class="n">dataset</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span>
            <span class="n">num_workers</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
            <span class="n">batch_sampler</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampler</span><span class="o">.</span><span class="n">Sampler</span><span class="p">,</span>
            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">AudioDataLoader</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
            <span class="n">dataset</span><span class="o">=</span><span class="n">dataset</span><span class="p">,</span>
            <span class="n">num_workers</span><span class="o">=</span><span class="n">num_workers</span><span class="p">,</span>
            <span class="n">batch_sampler</span><span class="o">=</span><span class="n">batch_sampler</span><span class="p">,</span>
            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
        <span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">collate_fn</span> <span class="o">=</span> <span class="n">_collate_fn</span></div>


<div class="viewcode-block" id="load_dataset"><a class="viewcode-back" href="../../../../modules/Data Loaders.html#openspeech.data.audio.data_loader.load_dataset">[docs]</a><span class="k">def</span> <span class="nf">load_dataset</span><span class="p">(</span><span class="n">manifest_file_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">list</span><span class="p">,</span> <span class="nb">list</span><span class="p">]:</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Provides dictionary of filename and labels.</span>

<span class="sd">    Args:</span>
<span class="sd">        manifest_file_path (str): evaluation manifest file path.</span>

<span class="sd">    Returns: target_dict</span>
<span class="sd">        * target_dict (dict): dictionary of filename and labels</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">audio_paths</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
    <span class="n">transcripts</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>

    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">manifest_file_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
        <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">()):</span>
            <span class="n">audio_path</span><span class="p">,</span> <span class="n">korean_transcript</span><span class="p">,</span> <span class="n">transcript</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
            <span class="n">transcript</span> <span class="o">=</span> <span class="n">transcript</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)</span>

            <span class="n">audio_paths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">audio_path</span><span class="p">)</span>
            <span class="n">transcripts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">transcript</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">audio_paths</span><span class="p">,</span> <span class="n">transcripts</span></div>
</pre></div>

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